SOTAVerified

Learning-To-Rank

Learning to rank is the application of machine learning to build ranking models. Some common use cases for ranking models are information retrieval (e.g., web search) and news feeds application (think Twitter, Facebook, Instagram).

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 126–150 of 753 papers

TitleStatusHype
Pairwise Judgment Formulation for Semantic Embedding Model in Web Search—0
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading BanditsCode0
Set2Seq Transformer: Learning Permutation Aware Set Representations of Artistic Sequences—0
Calibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential RecommendationCode0
Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank—0
Hidden or Inferred: Fair Learning-To-Rank with Unknown DemographicsCode0
MODRL-TA:A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search—0
Multi-objective Learning to Rank by Model Distillation—0
Leveraging Topic Specificity and Social Relationships for Expert Finding in Community Question Answering PlatformsCode0
Deep Domain Specialisation for single-model multi-domain learning to rank—0
When Search Engine Services meet Large Language Models: Visions and Challenges—0
Learning to Rank for Maps at Airbnb—0
Pistis-RAG: Enhancing Retrieval-Augmented Generation with Human Feedback—0
MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Model—0
Towards Explainable Test Case Prioritisation with Learning-to-Rank Models—0
GotFunding: A grant recommendation system based on scientific articles—0
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?Code0
Full Stage Learning to Rank: A Unified Framework for Multi-Stage Systems—0
Metalearners for Ranking Treatment Effects—0
RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank—0
Efficient and Responsible Adaptation of Large Language Models for Robust Top-k Recommendations—0
Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted TreesCode0
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor SearchCode0
Learning to rank quantum circuits for hardware-optimized performance enhancement—0
Chiplet Placement Order Exploration Based on Learning to Rank with Graph Representation—0
Show:102550
← PrevPage 6 of 31Next →

No leaderboard results yet.